“Files Used:”
Z:/COVID-19_WastewaterAnalysis/data/processed/MMSD_Interceptor_Cases_2_7_22.csv
Z:/COVID-19_WastewaterAnalysis/data/processed/LIMSWasteData_02-09-22.csv
MadDF <- filter(FullDF,Site=="Madison")%>%
mutate(FlagedOutliers = IdentifyOutliers(N1,Bin = 21, Action = "Flag"),
#Manual flagging that method misses due to boundary effect with binning
FlagedOutliers = ifelse(Date == mdy("01/26/2021") | Date == mdy("01/27/2021"),
TRUE, FlagedOutliers),
NoOutlierVar = ifelse(FlagedOutliers, NA, N1))
KeyOulierPoints <- c(ymd("2021-06-08"),
ymd("2021-10-17"),
ymd("2021-05-02"),
ymd("2021-01-26"))
NonOuliersDF <- MadDF%>%
mutate(Outlier = ifelse(FlagedOutliers,N1,NA))%>%
mutate(N1 = NoOutlierVar)%>%
filter(!(is.na(N1)))
OutLierPlotDF <- MadDF%>%
mutate(Outlier = ifelse(FlagedOutliers,N1,NA))%>%
mutate(N1 = NoOutlierVar)%>%
filter(!(is.na(N1)&is.na(Outlier)))%>%
ggplot(aes(x=Date))+#Data depends on time
geom_point(aes(y=N1,
color="N1",
info = N1),data=NonOuliersDF,size=.5)+#compares N1
geom_point(aes(y=Outlier,
color="N1 Outlier",
info = Outlier))+
theme_light() +
#scale_y_log10()+
scale_color_manual(values=c("#F8766D","#999999"))
ggplotly(OutLierPlotDF)
#"2021-06-08","2021-10-17","2021-05-02","2021-01-26"
SizeUsed = 1.5
alphaUsed = .9
N1ShapeUnit = 4
N2ShapeUnit = 5
IntercepterDF <- FullDF %>%
mutate(Pop = case_when(
Site=="MMSD-P2" ~ 83127,#111967
Site=="MMSD-P7" ~ 42734, #81977
Site=="MMSD-P8" ~ 70024,#127634
Site=="MMSD-P11" ~ 156651,#130799
Site=="MMSD-P18" ~ 89139,#151470
#Site=="Madison" ~ 603847,
))%>%
group_by(Site)%>%
mutate(FudgeFactor = mean(N1))%>%#Mean of sites to see if it works as normalizer
filter(Site != "Madison") #We are looking for agreement between the interceptors
#after some normalization so Madison just distracts
IntercepterOverLay <- IntercepterDF%>%
ggplot(aes(x=Date))+
geom_point(aes(y=N1,color = Site),size = SizeUsed, alpha= alphaUsed,shape=N1ShapeUnit)+
geom_point(aes(y=N2,color = Site),size = SizeUsed, alpha= alphaUsed,shape=N2ShapeUnit)+
theme_light() +
scale_y_log10()
ggplotly(IntercepterOverLay)
IntercepterOverLayFlow <- IntercepterDF%>%
ggplot(aes(x=Date))+
geom_point(aes(y=N1*FlowRate,color = Site),size = SizeUsed, alpha= alphaUsed,shape=N1ShapeUnit)+
geom_point(aes(y=N2*FlowRate,color = Site),size = SizeUsed, alpha= alphaUsed,shape=N2ShapeUnit)+
theme_light() +
scale_y_log10()
ggplotly(IntercepterOverLayFlow)
IntercepterOverLayPop <- IntercepterDF%>%
ggplot(aes(x=Date))+
geom_point(aes(y=N1/Pop,color = Site),size = SizeUsed, alpha= alphaUsed,shape=N1ShapeUnit)+
geom_point(aes(y=N2/Pop, color = Site),size = SizeUsed, alpha= alphaUsed,shape=N2ShapeUnit)+
theme_light() +
scale_y_log10()
ggplotly(IntercepterOverLayPop)
TieMethod <- "average"
IntercepterDF%>%
ggplot()+
geom_histogram(aes(x=log(N1)))+
facet_wrap(~Site)
IntercepterCoreDF <- IntercepterDF%>%
filter(!is.na(N1))%>%
group_by(Date)%>%
filter(n()==5)
IntercepterCoreDF%>%
group_by(Date)%>%
mutate(Ranking = frank(N1,ties.method = TieMethod))%>%
group_by(Site)%>%
summarize(N1AverageRanking = mean(Ranking, na.rm = TRUE))
## # A tibble: 5 x 2
## Site N1AverageRanking
## <chr> <dbl>
## 1 MMSD-P11 2.73
## 2 MMSD-P18 3.87
## 3 MMSD-P2 3.21
## 4 MMSD-P7 2.91
## 5 MMSD-P8 2.28
IntercepterCoreDF%>%
group_by(Date)%>%
mutate(Ranking = frank(N1*FlowRate, ties.method = TieMethod))%>%
group_by(Site)%>%
summarize(N1FlowAverageRanking = mean(Ranking, na.rm = TRUE))
## # A tibble: 5 x 2
## Site N1FlowAverageRanking
## <chr> <dbl>
## 1 MMSD-P11 3.26
## 2 MMSD-P18 4.60
## 3 MMSD-P2 2.92
## 4 MMSD-P7 2.02
## 5 MMSD-P8 2.20
IntercepterCoreDF%>%
group_by(Date)%>%
mutate(Ranking = frank(N1/Pop,ties.method = TieMethod))%>%
group_by(Site)%>%
summarize(N1PopAverageRanking = mean(Ranking, na.rm = TRUE))
## # A tibble: 5 x 2
## Site N1PopAverageRanking
## <chr> <dbl>
## 1 MMSD-P11 1.45
## 2 MMSD-P18 3.68
## 3 MMSD-P2 3.07
## 4 MMSD-P7 4.25
## 5 MMSD-P8 2.55
IntercepterCoreDF%>%
group_by(Date)%>%
mutate(Ranking = frank(N1, ties.method = TieMethod))%>%
group_by(Site,Ranking)%>%
ggplot()+
geom_histogram(aes(x=Ranking,fill=Site),position = "dodge")
IntercepterChangeDF <- IntercepterDF%>%
filter(!is.na(N1))%>%
mutate(ChangeN1 = lead(N1) - N1,
ChangeN2 = lead(N2) - N2,
PerChangeN1 = 200*(lead(N1) - N1)/(N1+lead(N1)),
PerChangeN2 = 200*(lead(N2) - N2)/(N2+lead(N2)))
IntercepterChangeOverLay <- IntercepterChangeDF%>%
ggplot(aes(x=Date))+
geom_point(aes(y = ChangeN1, color = Site), size = SizeUsed,
alpha = alphaUsed, shape=N1ShapeUnit)+
geom_point(aes(y = ChangeN2,color = Site), size = SizeUsed,
alpha = alphaUsed, shape = N2ShapeUnit)+
theme_light()#+
#scale_y_log10()
ggplotly(IntercepterChangeOverLay)
IntercepterPerChangeOverLay <- IntercepterChangeDF%>%
ggplot(aes(x = Date))+
geom_point(aes(y = PerChangeN1, color = Site), size = SizeUsed,
alpha = alphaUsed, shape = N1ShapeUnit)+
geom_point(aes(y = PerChangeN2, color = Site), size = SizeUsed,
alpha= alphaUsed,shape = N2ShapeUnit)+
theme_light()#+
#scale_y_log10()
ggplotly(IntercepterPerChangeOverLay)
LoessFunc <- function(SiteFilter,DF,SpanConstant = .163){
MainDF <- DF%>%
filter(Site==SiteFilter)
MainDF[["loessN1"]] <- loessFit(y=(MainDF[["N1"]]),
x=MainDF$Date, #create loess fit of the data
span=SpanConstant, #span of .2 seems to give the best result, not rigorously chosen
iterations=2)$fitted#2 iterations remove some bad patterns
return(MainDF)
}
SiteLoessDF <- lapply(c("MMSD-P11","MMSD-P18","MMSD-P2","MMSD-P7","MMSD-P8"),
LoessFunc,IntercepterDF,SpanConstant=.2)%>%
bind_rows()
A <- SiteLoessDF%>%
filter(!is.na(loessN1))%>%
ggplot(aes(x=Date))+
geom_point(aes(y=N1,color=Site),data=IntercepterDF,size=.5,alpha=.5)+
geom_line(aes(y=loessN1,color=Site))+
scale_y_log10()
ggplotly(A)
SevenDayMAFunc <- function(SiteFilter,DF){
MainDF <- DF%>%
filter(Site==SiteFilter)
MainDF[["SLDCases"]] <- rollapply(data = MainDF$FirstConfirmed, width = 7, FUN = mean,
na.rm = TRUE,fill=NA)
return(MainDF)
}
SiteLoessDF <- lapply(c("MMSD-P11","MMSD-P18","MMSD-P2","MMSD-P7","MMSD-P8"),
SevenDayMAFunc,IntercepterDF)%>%
bind_rows()
A <- SiteLoessDF%>%
filter(!is.na(SLDCases))%>%
ggplot(aes(x=Date))+
geom_point(aes(y=FirstConfirmed,color=Site,shape=Site),data=IntercepterDF,size=.5,alpha=.5)+
geom_line(aes(y=SLDCases,color=Site))+
scale_y_log10()
ggplotly(A)